ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On

• URL: http://arxiv.org/abs/2506.05858v1
• Date: Fri, 06 Jun 2025 08:26:39 GMT
• Title: ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On
• Authors: Jinjuan Wang, Wenzhang Sun, Ming Li, Yun Zheng, Fanyao Li, Zhulin Tao, Donglin Di, Hao Li, Wei Chen, Xianglin Huang,
• Abstract summary: Video virtual try-on aims to seamlessly replace the clothing of a person in a video with a target garment.<n>Existing approaches still struggle to maintain continuity and reproduce garment details.<n> ChronoTailor, a diffusion-based framework, generates temporally consistent videos while preserving fine-grained garment details.
• Score: 19.565037902386475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video virtual try-on aims to seamlessly replace the clothing of a person in a source video with a target garment. Despite significant progress in this field, existing approaches still struggle to maintain continuity and reproduce garment details. In this paper, we introduce ChronoTailor, a diffusion-based framework that generates temporally consistent videos while preserving fine-grained garment details. By employing a precise spatio-temporal attention mechanism to guide the integration of fine-grained garment features, ChronoTailor achieves robust try-on performance. First, ChronoTailor leverages region-aware spatial guidance to steer the evolution of spatial attention and employs an attention-driven temporal feature fusion mechanism to generate more continuous temporal features. This dual approach not only enables fine-grained local editing but also effectively mitigates artifacts arising from video dynamics. Second, ChronoTailor integrates multi-scale garment features to preserve low-level visual details and incorporates a garment-pose feature alignment to ensure temporal continuity during dynamic motion. Additionally, we collect StyleDress, a new dataset featuring intricate garments, varied environments, and diverse poses, offering advantages over existing public datasets, and will be publicly available for research. Extensive experiments show that ChronoTailor maintains spatio-temporal continuity and preserves garment details during motion, significantly outperforming previous methods.

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